Modern diffusion sensitised protocols may densely sample different orientations and b-values [1]}, [2]}, [3]}, employ multiple gradient and RF pulses within diffusion preparation [4]}, [5]}, [6]}, [7]}, utilise non-standard gradient pulses [8]}, [9]}, [1]}, [11]}, or incorporate interleaving and parallel imaging to speedup acquisitions, resulting in parameter variation between measurements (either by volume or by slice [12]}, [13]}). Processing data from these approaches and their combinations require detailed information about the diffusion preparation used in each image or volume. This demands more information than is currently supplied by the DICOM files output by scanners. Limited standardisation of data formats results in significant use of ad-hoc methods for communicating sequence parameters [1]}. For those producing and consuming archive-quality data, such methods are too variable to be reasonably programmed for, obstructing large scale analysis inherent to population studies, pipeline comparison, and machine learning [15]}, [16]}.
